Обновление от 08.01.2026
Инструкция предполагает, что ваш роутер сброшен до заводских настроек.
Инструкция проверена на RouterOS версии 7.20
| Product Year Version Product Keys | |
| Visual Studio 2026 18.x | |
| Professional: NVTDK-QB8J9-M28GR-92BPC-BTHXK | |
| Enterprise: VYGRN-WPR22-HG4X3-692BF-QGT2V | |
| https://x.com/massgravel/status/1988306014371008542 | |
| Visual Studio 2022 2021 17.x Professional: | |
| TD244-P4NB7-YQ6XK-Y8MMM-YWV2J | |
| Enterprise: |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1"> | |
| <title>Who are you?</title> | |
| <link rel="preconnect" href="https://fonts.googleapis.com"> | |
| <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> | |
| <link href="https://fonts.googleapis.com/css2?family=Patrick+Hand&display=swap" rel="stylesheet"> |
| #@runtime Jython | |
| # -*- coding: utf-8 -*- | |
| from ghidra.util.task import Task | |
| from ghidra.app.decompiler import DecompInterface | |
| from ghidra.util.task import ConsoleTaskMonitor | |
| from ghidra.program.model.symbol import SourceType, SymbolType | |
| from ghidra.program.model.pcode import HighFunctionDBUtil | |
| from ghidra.program.model.pcode.HighFunctionDBUtil import ReturnCommitOption | |
| from java.awt import BorderLayout | |
| from javax.swing import JButton, JFrame, JTextArea, JScrollPane, JPanel |
| { | |
| "files.associations": { | |
| "*.clj": "clojure", | |
| "*.cljc": "clojure", | |
| "*.cljs": "clojurescript", | |
| "*.edn": "edn" | |
| }, | |
| "editor.hover.enabled": true, | |
| "editor.hover.delay": 300, | |
| "problems.decorations.enabled": true, |
Recommendations of unit types per media type:
| Media | Recommended | Occasional use | Infrequent use | Not recommended |
|---|---|---|---|---|
| Screen | em, rem, % | px | ch, ex, vw, vh, vmin, vmax | cm, mm, in, pt, pc |
| em, rem, % | cm, mm, in, pt, pc | ch, ex | px, vw, vh, vmin, vmax |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.